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About This Role
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ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side\-by\-side with a powerful collective of thinkers and experts shaping life\-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client\-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life\-changing impact to ZS.
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Lead Revenue Operations Consultant
As a Revenue Operations Analyst within the ZAIDYN platform commercialization team, you will play a key role in building and scaling our go\-to\-market revenue operations capabilities.
This is a hands\-on “builder” role for an analytically driven operator who enjoys working across Sales, Marketing, and Customer Success. You will support GTM leaders by turning data into insights, helping create and iterate RevOps processes, and contributing to the evolution of our sales tools and operating rhythms as the business grows.
If you have experience in SaaS revenue operations, enjoy working with complex sales data, and want to make an impact in the life sciences innovation space, this role offers strong growth and visibility.
What You’ll Do
Forecasting, Pipeline \& Revenue Analytics
- Forecasting Support: Support weekly, monthly, and quarterly forecasting processes by analyzing pipeline health, deal movement, and risk factors.
- Funnel Analysis: Analyze the end\-to\-end revenue funnel (MQL Closed\-Won Renewal) to identify trends, drop\-offs, and opportunities to improve conversion and velocity.
- Reporting \& Dashboards: Build and maintain dashboards and reporting across pipeline, bookings, ARR/MRR, churn, and sales activity.
- + Insights \& Storytelling: Help translate performance data into clear, actionable insights for regular business reviews and internal stakeholders.
- RevOps Process \& Deal Support
+ Deal Support: Assist with non\-standard deal reviews by partnering with Sales, Finance, and Legal to ensure pricing, approvals, and contract terms align with company guidelines (including SaaS and hybrid models).
+ Sales Methodology Enablement: Help operationalize sales methodologies (e.g., MEDDPICC, Challenger) within the CRM and support adoption through reporting and process design.
+ Process Documentation \& Improvement: Support the design, documentation, and ongoing improvement of RevOps processes such as forecasting, deal desk workflows, and lead management.
+ Cross\-Functional Coordination: Partner with Product, Marketing, and Sales to help ensure new ZAIDYN offerings are supported with updated pricing, sales materials, and CRM configurations.
- Sales Technology \& Tools
+ CRM \& Tools Administration: Support the administration and ongoing improvement of the GTM tech stack (e.g., CRM, Outreach, Gong, LinkedIn Sales Navigator, Clari), focusing on usability and data quality.
+ Tool Optimization: Help evaluate and implement enhancements or new tools that improve seller productivity and reduce manual work.
- Performance Analytics \& Incentive Support
+ Quota \& Compensation Analysis: Assist with sales quota modeling and analysis of variable compensation plans to understand performance outcomes and behavior drivers.
+ Seller Productivity Reporting: Track metrics such as ramp time, attainment, pipeline coverage, and activity levels to support Sales Enablement and leadership planning.
What You’ll Bring* Experience: 5 \+ years of experience in Revenue Operations, Sales Operations, or GTM Analytics within a B2B SaaS environment; life sciences experience is a plus.
- Education: Bachelor’s degree in Business, Economics, Analytics, Data Science, or a related field.
- Technical Skills:
- Strong CRM experience (Microsoft Dynamics or Salesforce)
- Advanced Excel skills and comfort working with large datasets
- Experience with BI tools (Tableau, Power BI)
- Working knowledge of SQL preferred
- SaaS Knowledge: Solid understanding of SaaS metrics (ARR, NRR, CAC, LTV, churn) and exposure to SaaS \+ services business models.
- Builder Mindset: Comfortable working in evolving environments where processes are still being defined and improved.
- Analytical Curiosity: Enjoys digging into data to understand how the business operates and how ZAIDYN fits into the life sciences ecosystem.
- Collaborative \& Proactive: Able to work independently on projects while collaborating closely with cross\-functional and global teams.
- Growth\-Oriented: Interested in developing deeper expertise in RevOps, analytics, and commercial operations as the platform scales.
- Excellent verbal and written communication skills;
- Ability to work in a team environment. Global exposure or experience of having worked in global cross office teams is preferred;
- Able to apply high level critical thinking skills to understand and solve complex problems;
- Fluency in English.
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How you’ll grow:* Cross\-functional skills development \& custom learning pathways
- Milestone training programs aligned to career progression opportunities
- Internal mobility paths that empower growth via s\-curves, individual contribution and role expansions
Perks \& Benefits:
ZS offers a comprehensive total rewards package including health and well\-being, financial planning, annual leave, personal growth and professional development. Our robust skills development programs, multiple career progression options and internal mobility paths and collaborative culture empowers you to thrive as an individual and global team member.
Hybrid working model:
We are committed to giving our employees a flexible and connected way of working. A flexible and connected ZS allows us to combine work from home and on\-site presence at clients/ZS offices for the majority of our week. The magic of ZS culture and innovation thrives in both planned and spontaneous face\-to\-face connections. Travel:
Travel is a requirement at ZS for client facing ZSers; business needs of your project and client are the priority. While some projects may be local, all client\-facing ZSers should be prepared to travel as needed. Travel provides opportunities to strengthen client relationships, gain diverse experiences, and enhance professional growth by working in different environments and cultures. Considering applying?
At ZS, we honor the visible and invisible elements of our identities, personal experiences, and belief systems—the ones that comprise us as individuals, shape who we are, and make us unique. We believe your personal interests, identities, and desire to learn are integral to your success here. We are committed to building a team that reflects a broad variety of backgrounds, perspectives, and experiences. Learn more about our inclusion and belonging efforts and the networks ZS supports to assist our ZSers in cultivating community spaces and obtaining the resources they need to thrive.
If you’re eager to grow, contribute, and bring your unique self to our work, we encourage you to apply.
ZS is an equal opportunity employer and is committed to providing equal employment and advancement opportunities without regard to any class protected by applicable law.
To complete your application:
Candidates must possess or be able to obtain work authorization for their intended country of employment. An on\-line application, including a full set of transcripts (official or unofficial), is required to be considered.
NO AGENCY CALLS, PLEASE.
Find Out More At:
www.zs.com
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At ZS Associates, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
ZS Associates AI Hiring
ZS Associates has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span South San Francisco, CA, US, Evanston, IL, US, Princeton, NJ, US. Compensation range: $215K - $255K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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